The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration.

Most models of migration simply assume that migrants somehow make their way from their point of origin to their chosen destination. We know, however, that-especially in the case of asylum migration-the migrant journey often is a hazardous, difficult process where migrants make decisions based on limited information and under severe material constraints. Here we investigate the dynamics of the migration journey itself using a spatially explicit, agent-based model. In particular we are interested in the effects of limited information and information exchange. We find that under limited information, migration routes generally become suboptimal, their stochasticity increases, and migrants arrive much less frequently at their preferred destination. Under specific circumstances, self-organised consensus routes emerge that are largely unpredictable. Limited information also strongly reduces the migrants' ability to react to changes in circumstances. We conclude, first, that information and information exchange is likely to have considerable effects on all aspects of migration and should thus be included in future modelling efforts and, second, that there are many questions in theoretical migration research that are likely to profit from the use of agent-based modelling techniques.

Paleodemographic analysis of age at death for a population of Black Sea Scythians: An exploration by using Bayesian methods.

OBJECTIVES: Studies of the demography of past populations involving deterministic life tables can be criticized for ignoring the errors of estimation. Bayesian methods offer an alternative, by focusing on the uncertainty of the estimates, although their results are often sensitive to the choice of prior distributions. The aim of this study is to explore a range of Bayesian methods for estimating age at death for a population of nomadic warriors-Scythians from the Black Sea region. MATERIALS AND METHODS: In total, skeletons of 312 individuals (93 children and 219 adults) from Glinoe (Moldova), dated to the 5th-2nd century BCE, were examined. We unified the age categories corresponding to different aging methods, allowing an application of a probabilistic assessment of the age categorization. A hierarchical Bayesian multinomial-Dirichlet-Dirichlet model was applied, with a hypothetical, subjective reference population, a real reference population, and no reference. RESULTS: Stationary-population life expectancy was estimated as 27.7 years (95% CI: 25.1-30.3) for a newborn (e0 ), and 16.4 years (14.0-19.0) for 20-year-olds (e20 ), although with high uncertainty, and sensitive to the model specification. Slight differences in longevity between different social strata and between the Classical and Late chronological periods were found, although with high estimation errors. A more robust finding, confirming earlier studies, was a high probability of death in young adulthood, which could depend on Scythian lifestyle (conflicts, wars). DISCUSSION: Our study shows a way to overcome some limitations of broad age categorization by using the Bayesian approach with alternative model specifications, allowing to assess the impact of reference populations.

A Framework for Estimating Migrant Stocks Using Digital Traces and Survey Data: An Application in the United Kingdom.

An accurate estimation of international migration is hampered by a lack of timely and comprehensive data, and by the use of different definitions and measures of migration in different countries. In an effort to address this situation, we complement traditional data sources for the United Kingdom with social media data: our aim is to understand whether information from digital traces can help measure international migration. The Bayesian framework proposed is used to combine data from the Labour Force Survey (LFS) and the Facebook Advertising Platform to study the number of European migrants in the United Kingdom, with the aim of producing more accurate estimates of the numbers of European migrants. The overarching model is divided into a Theory-Based Model of migration and a Measurement Error Model. We review the quality of the LFS and Facebook data, paying particular attention to the biases of these sources. The results indicate visible yet uncertain differences between model estimates using the Bayesian framework and individual sources. Sensitivity analysis techniques are used to evaluate the quality of the model. The advantages and limitations of this approach, which can be applied in other contexts, are discussed. We cannot necessarily trust any individual source, but combining them through modeling offers valuable insights.

Choosing the choice: Reflections on modelling decisions and behaviour in demographic agent-based models.

This paper investigates the issues associated with choosing appropriate models of choice for demographic agent-based models. In particular, we discuss the importance of context, time preference, and dealing with uncertainty in decision modelling, as well as the heterogeneity between agents in their decision-making strategies. The paper concludes by advocating empirically driven, modular, and multi-model approaches to designing simulations of human decision-making, given the lack of an agreed strategy for dealing with any of these issues. Furthermore, we suggest that an iterative process of data collection and simulation experiments, with the latter informing future empirical data collection, should form the basis of such an endeavour. The discussion is illustrated with reference to selected demographic agent-based models, with a focus on migration.

Bayesian demography 250†years after Bayes.

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.

Simulation studies of social systems: telling the story based on provenance patterns.

Social simulation studies are complex. They typically combine various data sources and hypotheses about the system's mechanisms that are integrated by intertwined processes of model building, simulation experiment execution and analysis. Various documentation approaches exist to increase the transparency and traceability of complex social simulation studies. Provenance standards enable the formalization of information on sources and activities, which contribute to the generation of an entity, in a queryable and computationally accessible manner. Provenance patterns can be defined as constraints on the relationships between specific types of activities and entities of a simulation study. In this paper, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analysing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of an agent-based simulation project and by presenting its implementation in a web-based tool.

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe.

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers' journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011-21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

How safe is this trip? Judging personal safety in a pandemic based on information from different sources.

In a preregistered experiment, we presented participants with information about the safety of traveling during a deadly pandemic and during a migration trip using five different sources (a news article, a family member, an official organization, someone with personal experience, and the travel organizer) and four different verbal descriptions of the likelihood of safety (very likely, likely, unlikely, and very unlikely). We found that both for the pandemic and migration contexts, judgments about the likelihood of safely traveling and decisions to travel were most strongly influenced by information from the respective official organizations and that participants also indicated greater willingness to share information from official organizations with others. These results are consistent with the established finding that expert sources are more persuasive. However, we also found that, regardless of source, participants thought that it would be safe to travel even when told that it was unlikely or very unlikely to be safe. Additionally, participants did not discriminate between the grades of likelihood description (such as between likely and very likely or between unlikely and very unlikely), suggesting that in the contexts examined directionality matters much more than attempts to communicate more fine-grained likelihood information with verbal phrases. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A comparison of official population projections with Bayesian time series forecasts for England and Wales.

We compare official population projections with Bayesian time series forecasts for England and Wales. The Bayesian approach allows the integration of uncertainty in the data, models and model parameters in a coherent and consistent manner. Bayesian methodology for time-series forecasting is introduced, including autoregressive (AR) and stochastic volatility (SV) models. These models are then fitted to a historical time series of data from 1841 to 2007 and used to predict future population totals to 2033. These results are compared to the most recent projections produced by the Office for National Statistics. Sensitivity analyses are then performed to test the effect of changes in the prior uncertainty for a single parameter. Finally, in-sample forecasts are compared with actual population and previous official projections. The article ends with some conclusions and recommendations for future work.

Inferring transient dynamics of human populations from matrix non-normality.

In our increasingly unstable and unpredictable world, population dynamics rarely settle uniformly to long-term behaviour. However, projecting period-by-period through the preceding fluctuations is more data-intensive and analytically involved than evaluating at equilibrium. To efficiently model populations and best inform policy, we require pragmatic suggestions as to when it is necessary to incorporate short-term transient dynamics and their effect on eventual projected population size. To estimate this need for matrix population modelling, we adopt a linear algebraic quantity known as non-normality. Matrix non-normality is distinct from normality in the Gaussian sense, and indicates the amplificatory potential of the population projection matrix given a particular population vector. In this paper, we compare and contrast three well-regarded metrics of non-normality, which were calculated for over 1000 age-structured human population projection matrices from 42 European countries in the period 1960 to 2014. Non-normality increased over time, mirroring the indices of transient dynamics that peaked around the millennium. By standardising the matrices to focus on transient dynamics and not changes in the asymptotic growth rate, we show that the damping ratio is an uninformative predictor of whether a population is prone to transient booms or busts in its size. These analyses suggest that population ecology approaches to inferring transient dynamics have too often relied on suboptimal analytical tools focussed on an initial population vector rather than the capacity of the life cycle to amplify or dampen transient fluctuations. Finally, we introduce the engineering technique of pseudospectra analysis to population ecology, which, like matrix non-normality, provides a more complete description of the transient fluctuations than the damping ratio. Pseudospectra analysis could further support non-normality assessment to enable a greater understanding of when we might expect transient phases to impact eventual population dynamics.

Integrated modelling of age and sex patterns of European migration.

Age and sex patterns of migration are essential for understanding drivers of population change and heterogeneity of migrant groups. We develop a hierarchical Bayesian model to estimate such patterns for international migration in the European Union and European Free Trade Association from 2002 to 2008, which was a period of time when the number of members expanded from 19 to 31 countries. Our model corrects for the inadequacies and inconsistencies in the available data and estimates the missing patterns. The posterior distributions of the age and sex profiles are then combined with a matrix of origin-destination flows, resulting in a synthetic database with measures of uncertainty for migration flows and other model parameters.

Bayesian Population Forecasting: Extending the Lee-Carter Method.

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

Letter to the Editor: Probabilistic population forecasts for informed decision making.

Demographic forecasts are inherently uncertain. Nevertheless, an appropriate description of this uncertainty is a key underpinning of informed decision making. In recent decades various methods have been developed to describe the uncertainty of future populations and their structures, but the uptake of such tools amongst the practitioners of official population statistics has been lagging behind. In this letter we revisit the arguments for the practical uses of uncertainty assessments in official population forecasts, and address their implications for decision making. We discuss essential challenges, both for the forecasters and forecast users, and make recommendations for the official statistics community.

Population and labour force projections for 27 European countries, 2002-052: impact of international migration on population ageing: Projections de population et de population active pour 27 pays européens 2002-052: impact de la migration internationale sur le vieillissement de la population.

Population and labour force projections are made for 27 selected European countries for 2002-052, focussing on the impact of international migration on population and labour force dynamics. Starting from single scenarios for fertility, mortality and economic activity, three sets of assumptions are explored regarding migration flows, taking into account probable policy developments in Europe following the enlargement of the EU. In addition to age structures, various support ratio indicators are analysed. The results indicate that plausible immigration cannot offset the negative effects of population and labour force ageing.